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```LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034
M.Sc. DEGREE EXAMINATION – MATHEMATICS
AC 56
FOURTH SEMESTER – APRIL 2007
ST 4900 - MATHEMATICAL STATISTICS - II
Date & Time: 25/04/2007 / 9:00 - 12:00
Dept. No.
Max. : 100 Marks
SECTION-A (10 x 2 = 20)
Answer ALL the questions. Each carries 2 marks.
1. Distinguish between point and interval estimation.
2. Examine whether the sample variance is a biased estimator of 2 when a random sample of size
‘n’ is drawn from N(, 2 ).
3. When do you say that a statistic is a consistent estimator of a parameter?
4. Distinguish between power function and power of the test.
5. What is degree of freedom?
6. Illustrate graphically the meaning of UMPT.
7. What do you understand by likelihood ratio tests?
8. Define F statistic.
9. Define: Markov Chain.
10. Classify the stochastic processes with respect to time and state space.
SECTION-B (5 x 8 = 40)
Answer any FIVE questions. Each carries 8 marks.
11. Let Y1 and Y2 be two independent unbiased estimators of θ. Let the variance of Y1 be twice the
variance of Y2. Find the constants k1 and k2 so that k1 Y1 + k2 Y2 is an unbiased estimator with
smallest possible variance for such a linear combination.
12. State and prove Rao-Cramer Inequality.
13. Let X1, …, Xn be i.i.d., each with distribution having p.d.f.
f(x; θ1, θ2 ) =
(1 / θ2) exp{ - ( x- θ1 ) /θ2}, -θ1 ≤ x< ∞, -∞ < θ1 < ∞, 0 <θ2< ∞,
0; elsewhere.
Find the maximum likelihood estimators of θ1 and θ2.
14. Let X ≥ 1 be the critical region for testing H0: θ = 1 against H1: θ = 2 on the basis of a single
observation from the population with pdf
f(x ,θ) = θ exp{ - θ x }, 0 < x <∞; 0 otherwise.
Obtain the size and power of the test.
15. Let X1, X2…Xn be iid U(0,), >0. Show that the family of distributions has MLR in X(n).
1
16. A random sample of size 14 drawn from a normal population provides a sample mean of
3.22mm with an unbiased standard deviation of 0.34mm. Can you conclude at 5% level of
significance that it meets the company’s specification of 2.7mm against more than 2.7mm?
Construct 95% confidence limits for the population mean.
17. Let X1, X2,…,Xn be iid N(,2). Derive LRT for testing H0:  =  0 against
H1:   0, 2 is unknown.
18. Describe Poisson process.
SECTION-C (2 x 20 =40)
Answer any TWO questions. Each carries 20 marks.
19. a) State and prove factorization criterion for determining sufficient statistics. (12)
b) Show that the first order statistic Y1 of a random sample of size n from the
distribution having p.d.f.
f(x: θ) = e–(x - θ), - < x < , -  < θ < , zero elsewhere,
is a complete sufficient statistic for θ.
Find the unique function of this statistic, which is the unbiased minimum variance
estimator of θ.
(8)
20. State and prove the necessary and sufficient conditions of Neyman-Pearson
Fundamental Lemma.
(10+10)
21. a) Let X1,X2 and X3 be a sample of size 3 from Poisson distribution with mean θ.
Consider the problem of testing H0: θ = 2 against H1: θ = 3. Find the randomized
MPT of level α =0.05.
(12)
b) Prove or disprove:
“UMPT of level α always exists for all types of testing problems”.
(8)
22. a) A number is to be selected from the interval (x : 0 < x < 2) by a random process.
Let Ai = {(x : (i-1)/2 < x < i/2}, i = 1,2,3, and let A4 = {x : 3/2 < x < 2}. A certain
hypothesis assigns probabilities pio to these sets in accordance with
pio = Ai (1/2) (2-x) dx, i = 1,2,3,4. If the observed frequencies of the
sets Ai, i = 1,2,3,4, are respectively, 30, 30, 10, 10, would Ho be accepted at the
(approximate) 5 percent level of significance ?
(10)
b) Consider a Markov chain having state space S = {0,1,2} and transition probability
matrix
1/3
1/3
1/3
¼
½
¼
1/6
1/3
½
Show that this chain has a unique stationary distribution  and find .
******
(10)
2
```
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